Create README.md

README.md

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+---
+license: apache-2.0
+tags:
+- vision
+- image-classification
+datasets:
+- imagenet-1k
+widget:
+- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
+  example_title: Tiger
+- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
+  example_title: Teapot
+- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
+  example_title: Palace
+---
+
+# EfficientNet (b2 model) 
+
+EfficientNet model trained on ImageNet-1k at resolution 260x260. It was introduced in the paper [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
+](https://arxiv.org/abs/1905.11946) by Mingxing Tan and Quoc V. Le, and first released in [this repository](https://github.com/keras-team/keras). 
+
+Disclaimer: The team releasing EfficientNet did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+## Model description
+
+EfficientNet is a mobile friendly pure convolutional model (ConvNet) that proposes a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient.
+
+![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/efficientnet_architecture.png)
+
+## Intended uses & limitations
+
+You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=efficientnet) to look for
+fine-tuned versions on a task that interests you.
+
+### How to use
+
+Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
+
+```python
+import torch
+from datasets import load_dataset
+from transformers import EfficientNetImageProcessor, EfficientNetForImageClassification
+
+dataset = load_dataset("huggingface/cats-image")
+image = dataset["test"]["image"][0]
+
+preprocessor = EfficientNetImageProcessor.from_pretrained("google/efficientnet-b2")
+model = EfficientNetForImageClassification.from_pretrained("google/efficientnet-b2")
+
+inputs = preprocessor(image, return_tensors="pt")
+
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+# model predicts one of the 1000 ImageNet classes
+predicted_label = logits.argmax(-1).item()
+print(model.config.id2label[predicted_label]),
+```
+
+For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/efficientnet).
+
+### BibTeX entry and citation info
+
+```bibtex
+@article{Tan2019EfficientNetRM,
+  title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks},
+  author={Mingxing Tan and Quoc V. Le},
+  journal={ArXiv},
+  year={2019},
+  volume={abs/1905.11946}
+}
+```